Web search using personalized user conceptual index

Proceedings of the 2nd Indian International Conference on Artificial Intelligence, IICAI 2005

Volumn , Issue , 2005, Pages 1719-1728

  Sendhilkumar, S ^a   Geetha, T V ^a  

^a ANNA UNIVERSITY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION NEED; INFORMATION OVERLOADS; NEW INDICES; PERSONALIZATIONS; SEMI-STRUCTURED; STRUCTURED INFORMATION; WEB ENVIRONMENT; WEB SEARCHES; WEB USERS;

ARTIFICIAL INTELLIGENCE;

WEBSITES;

EID: 38149005453     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. J.L. Herlocker, J.A. Konstan, A. Borchers, and J. Riedl: An algorithmic framework for performing collaborative filtering. In ACM SIGIR (1999).
2. B. Sarwar, G. Kaiypis, J. A. Konstan. and J. Riedl: Analysis of recommendation algorithms for ecommerce. In ACM'OO Conference on Electronic Commerce, pages (2000) 158-167.
3. J. Strivastrava, R.t C'ooley, M. Deshpande. and P-N. Tan: Web usage mining: Discover and applications of usage patterns from web data. SIGKDD Explorations. 1(2). Jan. (2000).
4. B. Mobasher. R. Cooley and J. Srivastava: Automatic personalization based on Web usage mining. In Communications of the ACM, (43) S. August (2000).
5. B. Mobasher, H. Dai, T. Luo, and M. Nakagawa: Effective personalization based on association rule discovery from web usage data. In Web Information and Data Management, (2001)9-15.
6. B. Mobasher. H. Dai, T.Luo, and M. Nakagawa: Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery (2002) 6:61-82.
7. M. Claypool, A. Gokhale, T. Miranda, P. Mumikov, D. Netes. and M. Sartin: Combining Content-based and Collaborative Filters in an Online Newspaper. In Proceedings of the ACM SIGIR '99 Workshop on Recommender Systems: Algorithms and Evaluation. University of California. Berkeley. Aug. (1999).
8. M. Pazzani. A Framework for Collaborative. Content-Based and Demographic Filtering. Artificial Intelligence Review, Dec. (1999) 393-408.
9. Cyrus Shahabi and Famoush Banaei-Kashani: A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking (2004).
10. Greenberg S. and A. Cockbum: Getting Back to Back: Alternate Behaviors for a Web Browser's Back Button. Proceedings of the 5th Annual Human Factors and Web Conference. NIST. Gaithersburg. Maryland, June (1999).
11. Cooley R., B. Mobasher. and J. Srivastava: Data Preparation for Mining World Wide Web Browsing Patterns. Journal of Knowledge and Information Systems. Springer-Verlag. February (1999) 1(1): 5-32.
12. Konstan J., B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl: Applying Collaborative Filtering to Usenet News. Communications of the ACM (40) 3 (1997).
13. Pitkow J.E.: In Search of Reliable Usage Data on the WWW The Sixth International World Wide Web Conference. Santa Clara. California (1997).
14. Shahabi C., A Zarkesh, J. Adibi, V. Shah: Knowledge Discovery from Users Web-Page Navigation Proceedings of the IEEE RIDE97 Workshop. April (1997).
15. Levene L.. and G. Loizou: Zipfs law for web surfers. Knowledge and Information Systems an International Journal (2000).
16. Catledge L. and J. Pitkow: Characterizing Browsing Behaviors on the World Wide Web, Computer Networks and ISDN Systems, 27(6) (1995).
17. Honghua (Kathy) Dai., Bamshad Mobasher: A Road map to More Elective Web Personalization: Integrating Domain Knowledge with Web Usage Mining (2004) 18. Jeh G., Widom J.: Scaling Personalized Web Search (2003).
18. Magdalini E. and Michalis V.: Web Mining for Web Personalization. ACM Transactions on Internet Technology. Volume 3, Issue 1, (2002) 1 - 27.
19. Gauch S., Chaffee J., Pretschner A.: Ontology Based Personalized Search. Proceedings of the 11th EEEE International Conference on Tools with Artificial Intelligence. (2004) P: 391.
20. Trajkova J. and Gaush S.: Improving Ontology-Based User Profiles. M.S. Thesis. EEC'S, University of Kansas. Aug (2003).